Nicotine addiction continues to be a major problem in the US with smoking accounting for the loss of 443,000 lives and an estimated $193 billion economic burden each year. These staggering statistics speak to important of identifying factors that contribute to nicotine addiction. Cognitive deficits such as disrupted learning are a major symptom of nicotine withdrawal, yet little is understood about the genetic factors that contribute to nicotine withdrawal-related changes in cognition. Identifying the neural and genetic substrates of nicotine withdrawal deficits in cognition will provide data to facilitate drug discovery and identify markers for at risk populations. Use of genetic data to identify at risk populations could also facilitate development of treatments that could increase the likelihood of maintaining abstinence. Recent data from a study examining nicotine withdrawal-related changes in hippocampus-dependent learning in mice demonstrated that genetics contributes to this cognitive withdrawal phenotype. As the specific genes involved in this withdrawal phenotype are unknown, it is the goal of this application to use next-generation sequencing to identify gene variants related to nicotine withdrawal deficits in learning. Studies in the proposal will use the recombinant inbred (RI) BXD strain to characterize phenotypic differences across BXD lines (Aim 1) and use that data for quantitative trait loci (QTL) analysis to identify genes associated with the nicotine withdrawal learning deficit phenotype (Aim 2). In addition, BXD databases will be examined to identify other phenotypes that are genetically correlated with the cognitive withdrawal phenotype (Aim 2). Finally, RNA-sequencing in BXD lines that show extreme phenotypic variation will be used to identify changes in the transcriptome associated with the nicotine withdrawal deficit in learning phenotype. These studies should significantly advance understanding of and treatment of nicotine addiction. Identifying genes and gene expression changes that are associated with nicotine withdrawal deficits in learning in the mouse will facilitate identifying if homologous genes in humans that are associated with nicotine withdrawal deficits in cognition. This could help practitioners tailor treatments to patients. In addition, identifying specific changes in the transcriptome associated with nicotine withdrawal deficits in learning will provide valuable information on the changes in cell signaling that contribute to nicotine withdrawal deficits in learning and this should aid in identifying potential targets for pharmacotherapeutic development.